Analyze Data
SQL joins disparate datasets
Queries expose relationship
Adjust columns for table/graph needs
Department of Energy’s EIA: Monthly Energy Review &
NOAA Mauna Loa, Hawaii Observatory - Carbon PPM
SELECT p.date_year, p.date_month, CONCAT(p.date_year, '-', p.date_month, '-01')::date as date_day,
p.average_ppm as "carbon ppm", c.energy_consumed as "btu consumed", e.energy_co2 as "co2 emissions"
FROM ppm_month p
JOIN consumption c
ON p.date_year = c.date_year AND p.date_month = c.date_month and c.msn = 'TXRCBUS'
JOIN us_co2_emissions e
ON p.date_year = e.date_year AND p.date_month = e.date_month and e.msn = 'TETCEUS'
ORDER BY p.date_year, p.date_month
SELECT p.date_year,
sum(p.average_ppm) as carbon_ppm_total,
avg(p.average_ppm) as carbon_ppm_mean,
sum(c.energy_consumed) as btu_consumed_total,
avg(c.energy_consumed) as btu_consumed_mean,
sum(e.energy_co2) as co2_emissions_total,
avg(e.energy_co2) as co2_emissions_mean
FROM ppm_month p
JOIN consumption c
ON p.date_year = c.date_year AND p.date_month = c.date_month and c.msn = 'TXRCBUS'
JOIN us_co2_emissions e
ON p.date_year = e.date_year AND p.date_month = e.date_month and e.msn = 'TETCEUS'
GROUP BY p.date_year
ORDER BY p.date_year
kable_styling(kable(tail(agg_df, 10)),
bootstrap_options = c("striped", "hover"))
| |
date_year |
carbon_ppm_total |
carbon_ppm_mean |
btu_consumed_total |
btu_consumed_mean |
co2_emissions_total |
co2_emissions_mean |
| 38 |
2010 |
4678.79 |
389.8992 |
6641.355 |
553.4462 |
5585.741 |
465.4784 |
| 39 |
2011 |
4699.83 |
391.6525 |
6473.666 |
539.4722 |
5446.133 |
453.8444 |
| 40 |
2012 |
4726.24 |
393.8533 |
5684.503 |
473.7086 |
5237.300 |
436.4417 |
| 41 |
2013 |
4758.25 |
396.5208 |
6689.368 |
557.4473 |
5363.018 |
446.9182 |
| 42 |
2014 |
4783.77 |
398.6475 |
7007.139 |
583.9283 |
5411.193 |
450.9327 |
| 43 |
2015 |
4810.01 |
400.8342 |
6465.092 |
538.7577 |
5264.776 |
438.7313 |
| 44 |
2016 |
4850.87 |
404.2392 |
6033.098 |
502.7582 |
5172.402 |
431.0335 |
| 45 |
2017 |
4878.64 |
406.5533 |
6111.580 |
509.2983 |
5130.589 |
427.5491 |
| 46 |
2018 |
4902.26 |
408.5217 |
6896.516 |
574.7097 |
5267.750 |
438.9792 |
| 47 |
2019 |
410.83 |
410.8300 |
1163.200 |
1163.2000 |
493.839 |
493.8390 |
metric_ts <- xts(x=metrics_df[c("carbon ppm", "btu consumed", "co2 emissions")],
order.by=metrics_df$date_day)
plot(metric_ts, main = "Energy and Carbon PPM Metrics",
legend.loc="bottomright",
col = seaborn_palette[1:3],
yaxis.right=FALSE,
axes=FALSE,
lwd=1,
cex.main=3,
major.ticks="years",
major.format="%Y",
minor.format="%Y",
grid.ticks.lty=1)

Seasonal Decomposition
carbonppm_ts <- ts(metrics_df$`carbon ppm`, start=c(1973, 1), frequency=12)
carbonppm_stl <- stl(carbonppm_ts, s.window="periodic")
plot(carbonppm_stl, col = seaborn_palette[1],
main="Seasonal Decomposition of Global Carbon PPM")

consumed_ts <- ts(metrics_df$`btu consumed`, start=c(1973, 1), frequency=12)
consumed_stl <- stl(consumed_ts, s.window="periodic")
plot(consumed_stl, col = seaborn_palette[2],
main="Seasonal Decomposition of U.S. Energy Consumption")

emissions_ts <- ts(metrics_df$`co2 emissions`, start=c(1973, 1), frequency=12)
emissions_stl <- stl(emissions_ts, s.window="periodic")
plot(emissions_stl, col = seaborn_palette[3],
main = "Seasonal Decomposition of U.S. CO2 Emissions")

sql <- "WITH c1 AS
(SELECT CONCAT((date_year/10)::int * 10, 's') as decade,
REPLACE(description, 'Primary Energy Consumed by the ', '') AS \"Sector\",
SUM(energy_consumed) AS \"Primary Energy Consumed\"
FROM consumption
WHERE msn IN ('TXICBUS', 'TXRCBUS', 'TXACBUS', 'TXCCBUS', 'TXEIBUS')
GROUP BY CONCAT((date_year/10)::int * 10, 's'), description
), c2 AS
(SELECT CONCAT((date_year/10)::int * 10, 's') as decade,
REPLACE(description, 'Total Energy Consumed by the ', '') AS \"Sector\",
SUM(energy_consumed) AS \"Total Energy Consumed\"
FROM consumption
WHERE msn IN ('TECCBUS', 'TEACBUS', 'TEICBUS', 'TERCBUS')
GROUP BY CONCAT((date_year/10)::int * 10, 's'), description
)
SELECT c1.decade, c1.\"Sector\", c1.\"Primary Energy Consumed\", c2.\"Total Energy Consumed\"
FROM c1
LEFT JOIN c2
ON c1.\"Sector\" = c2.\"Sector\" AND c1.decade = c2.decade
ORDER BY c1.decade, c1.\"Sector\"
"
consumed_df <- dbGetQuery(conn, sql)
kable_styling(kable(tail(consumed_df)),
bootstrap_options = c("striped", "hover"))
| |
decade |
Sector |
Primary Energy Consumed |
Total Energy Consumed |
| 35 |
2000s |
Transportation Sector |
546616.33 |
548112.4 |
| 36 |
2010s |
Commercial Sector |
77171.71 |
326503.7 |
| 37 |
2010s |
Electric Power Sector |
693591.66 |
NA |
| 38 |
2010s |
Industrial Sector |
388661.21 |
570178.3 |
| 39 |
2010s |
Residential Sector |
117157.95 |
378492.5 |
| 40 |
2010s |
Transportation Sector |
490149.36 |
491557.4 |
Consumption and CO2 Emissions
plot_mat <- with(subset(consumed_df, Sector != 'Electric Power Sector'),
tapply(`Total Energy Consumed`, list(decade, `Sector`), sum))
par(mar=c(5, 5, 2, 1))
barplot(plot_mat, main="U.S. Total Energy Consumption by Sector", cex.main=1.5,
col=seaborn_palette[1:8], ylim=c(0, 8E5), xaxt="n", yaxt="n", beside=TRUE)
axis(side=1, at=c(5, 14, 23, 32), labels=colnames(plot_mat), padj=0.9, pos=c(0,0))
axis(side=2, at=axTicks(2), labels=format(axTicks(2), big.mark=',', scientific=FALSE), las=1)
legend("top", row.names(plot_mat), fill=seaborn_palette[1:8], ncol=8)

plot_mat <- with(consumed_df, tapply(`Primary Energy Consumed`, list(decade, `Sector`), sum))
par(mar=c(5, 5, 2, 1))
barplot(plot_mat, main="U.S. Primary Energy Consumption by Sector", cex.main=1.5,
col=seaborn_palette[1:8], ylim=c(0, 9E5), xaxt="n", yaxt="n", beside=TRUE)
axis(side=1, at=c(5, 14, 23, 32, 41), labels=colnames(plot_mat), padj=0.9, pos=c(0,0))
axis(side=2, at=axTicks(2), labels=format(axTicks(2), big.mark=',', scientific=FALSE), las=1)
legend("top", row.names(plot_mat), fill=seaborn_palette[1:8], ncol=8)

sql <- "SELECT CONCAT((date_year/10)::int * 10, 's') as decade,
REPLACE(REPLACE(description, 'Total Energy ', ''), ' CO2 Emissions', '') AS \"Sector\",
SUM(energy_co2) AS \"Total CO2 Emissions\"
FROM us_co2_emissions
WHERE msn IN ('TERCEUS', 'TECCEUS', 'TEACEUS', 'TXEIEUS')
GROUP BY date_year, description
ORDER BY date_year, description
"
emissions_df <- dbGetQuery(conn, sql)
kable_styling(kable(tail(emissions_df)),
bootstrap_options = c("striped", "hover"))
| |
decade |
Sector |
Total CO2 Emissions |
| 183 |
2010s |
Residential Sector |
2034.691 |
| 184 |
2010s |
Transportation Sector |
3830.401 |
| 185 |
2010s |
Commercial Sector |
164.804 |
| 186 |
2010s |
Electric Power Sector |
274.988 |
| 187 |
2010s |
Residential Sector |
223.165 |
| 188 |
2010s |
Transportation Sector |
295.360 |
plot_mat <- with(emissions_df, tapply(`Total CO2 Emissions`, list(decade, `Sector`), sum))
par(mar=c(5, 8, 2, 1))
barplot(plot_mat, main="U.S. CO2 Emissions by Sector", cex.main=1.5,
col=seaborn_palette[1:5], ylim=c(0, 6E4), xaxt="n", yaxt="n", beside=TRUE)
axis(side=1, at=c(3, 9, 15, 21)+0.5, labels=colnames(plot_mat), padj=0.9, pos=c(0,0))
axis(side=2, at=axTicks(2), labels=format(axTicks(2), big.mark=',', scientific=FALSE), las=1)
legend("top", row.names(plot_mat), fill=seaborn_palette[1:5], ncol=5)

sql <- "WITH sub AS
(SELECT CONCAT((date_year/10)::int * 10, 's') as decade,
REPLACE($1, '%', '') as sector,
REPLACE(
REPLACE(
REPLACE(
REPLACE(description, 'Commercial Sector CO2 Emissions', ''),
'Residential Sector CO2 Emissions', ''
), 'Transportation Sector CO2 Emissions', ''),
' ', '\n') AS \"Type\",
energy_co2
FROM us_co2_emissions
WHERE description LIKE $2)
SELECT decade, sector, \"Type\",
SUM(energy_co2) AS \"Total CO2 Emissions\"
FROM sub
GROUP BY decade, sector, \"Type\"
ORDER BY decade, sector, \"Type\"
"
params <- paste0(c("%Transportation", "%Residential", "%Commercial"), " Sector%")
emissions_type_df <- do.call(rbind, lapply(params, function(p) dbGetQuery(conn, sql, param=list(p, p))))
par(mfrow=c(3,2), mar=c(5, 5, 2, 1), mai = c(0.7, 0.2, 0.7, 0.2))
output <- by(emissions_type_df, emissions_type_df$sector, function(sub) {
plot_mat <- with(sub, tapply(`Total CO2 Emissions`, list(decade, `Type`), sum))
barplot(plot_mat, main=paste("U.S. CO2 Emissions by", sub$sector[[1]]), cex.main=1.5,
col=seaborn_palette[1:5], ylim=c(0, ceiling(max(plot_mat, na.rm=TRUE)/1E4) * 1E4),
xaxt="n", yaxt="n", beside=TRUE)
axis(side=1, at=c(3,9,15,21,27,33,39,45,51,57,63)[seq_along(colnames(plot_mat))] + 0.5,
labels=colnames(plot_mat), padj=0.9, pos=c(0,0))
axis(side=2, at=axTicks(2), labels=format(axTicks(2), big.mark=',', scientific=FALSE), las=1)
legend("top", row.names(plot_mat), fill=seaborn_palette[1:5], ncol=5)
})

Renewable Energy: Production and Consumption
sql <- "SELECT energy_type,
date,
SUM(production) AS production,
SUM(consumption) AS consumption
FROM us_renewable_energy
GROUP BY energy_type,
date
ORDER BY energy_type,
date
"
renewable_df <- dbGetQuery(conn, sql)
kable_styling(kable(tail(renewable_df)),
bootstrap_options = c("striped", "hover"))
| |
energy_type |
date |
production |
consumption |
| 4678 |
Wood Energy |
2019-02-01 |
190.887 |
182.491 |
| 4679 |
Wood Energy |
2019-03-01 |
198.621 |
191.507 |
| 4680 |
Wood Energy |
2019-04-01 |
195.791 |
187.670 |
| 4681 |
Wood Energy |
2019-05-01 |
201.743 |
193.775 |
| 4682 |
Wood Energy |
2019-06-01 |
198.379 |
189.036 |
| 4683 |
Wood Energy |
2019-07-01 |
205.023 |
196.873 |
par(mfrow=c(3,3), mar=c(5, 5, 2, 1))
output <- by(renewable_df, renewable_df$energy_type, function(sub) {
metric_ts <- xts(x=sub[c("production", "consumption")],
order.by=sub$date)
print(plot(metric_ts, main = sub$energy_type[1],
legend.loc="bottomright",
col = seaborn_palette[1:3],
yaxis.right=FALSE,
axes=FALSE,
lwd=1,
cex.main=3,
major.ticks="years",
major.format="%Y",
minor.format="%Y",
grid.ticks.lty=1))
})

U.S. Geological Survey - Groundwater Well Depth Data
sql <- "SELECT g.year, g.month,
avg(g.mean_value) as mean_value
FROM groundwater g
WHERE g.year BETWEEN 1990 AND 2019
GROUP BY g.year, g.month"
groundwater_df <- dbGetQuery(conn, sql)
kable_styling(kable(tail(groundwater_df)),
bootstrap_options = c("striped", "hover"))
| |
year |
month |
mean_value |
| 350 |
2019 |
2 |
63735.69 |
| 351 |
2019 |
3 |
60792.13 |
| 352 |
2019 |
4 |
54698.44 |
| 353 |
2019 |
5 |
53237.05 |
| 354 |
2019 |
6 |
49160.99 |
| 355 |
2019 |
7 |
76941.00 |
boxplot(mean_value ~ year, groundwater_df, col=seaborn_palette[1:10],
main="Groundwater Well Depth Mean Values", cex.main=1.5)

sql <- "WITH sites AS (
SELECT CONCAT(g.year, '-', g.month, '-1')::date AS \"date\",
g.site_name,
AVG(g.mean_value) as mean_value
FROM groundwater g
WHERE g.year BETWEEN 1990 AND 2019
GROUP BY CONCAT(g.year, '-', g.month, '-1')::date,
g.site_name
), bottom AS (
SELECT site_name, AVG(mean_value) AS mean_value, 'bottom_sites' AS category
FROM sites
GROUP BY site_name
ORDER BY AVG(mean_value) ASC LIMIT 5
), top AS (
SELECT site_name, AVG(mean_value) AS mean_value, 'top_sites' AS category
FROM sites
GROUP BY site_name
ORDER BY AVG(mean_value) DESC LIMIT 5
)
SELECT sites.\"date\", sites.mean_value, sites.site_name, top.category
FROM sites
INNER JOIN top
ON sites.site_name = top.site_name
UNION
SELECT sites.\"date\", sites.mean_value, sites.site_name, bottom.category
FROM sites
INNER JOIN bottom
ON sites.site_name = bottom.site_name
ORDER BY category, site_name, \"date\""
groundwater_df <- dbGetQuery(conn, sql)
kable_styling(kable(head(groundwater_df)),
bootstrap_options = c("striped", "hover"))
| date |
mean_value |
site_name |
category |
| 1991-06-01 |
31 |
FAIRPLAINS 2 WELL~ ST. CROIX~ USVI |
bottom_sites |
| 1991-07-01 |
35 |
FAIRPLAINS 2 WELL~ ST. CROIX~ USVI |
bottom_sites |
| 1991-08-01 |
41 |
FAIRPLAINS 2 WELL~ ST. CROIX~ USVI |
bottom_sites |
| 1991-09-01 |
42 |
FAIRPLAINS 2 WELL~ ST. CROIX~ USVI |
bottom_sites |
| 1991-10-01 |
36 |
FAIRPLAINS 2 WELL~ ST. CROIX~ USVI |
bottom_sites |
| 1991-11-01 |
40 |
FAIRPLAINS 2 WELL~ ST. CROIX~ USVI |
bottom_sites |
groundwater_df$year <- format(groundwater_df$date, "%Y")
par(mfrow=c(5,2), mar=c(5, 5, 2, 1))
output <- by(groundwater_df, groundwater_df$site_name, function(sub) {
metric_ts <- xts(x=sub[c("mean_value")],
order.by=sub$date)
print(plot(metric_ts, main = paste(sub$site_name[1], ':', sub$category[1]),
legend.loc="bottomright",
col = seaborn_palette[1:3],
yaxis.right=FALSE,
axes=FALSE,
lwd=1,
cex.main=3,
major.ticks="years",
major.format="%Y",
minor.format="%Y",
grid.ticks.lty=1))
})

U.S. FWS Endangered Species List
sql <- "SELECT CONCAT((f.date_year/10)::int * 10, 's') AS decade,
f.taxonomic_group,
f.current_status,
count(*) AS species_count
FROM fws_species_year f
WHERE f.current_status IN ('Endangered', 'Recovery', 'Resolved Taxon',
'Threatened', 'Extinction')
GROUP BY CONCAT((f.date_year/10)::int * 10, 's'),
f.taxonomic_group,
f.current_status
ORDER BY CONCAT((f.date_year/10)::int * 10, 's'),
f.taxonomic_group,
f.current_status"
fws_df <- dbGetQuery(conn, sql)
kable_styling(kable(tail(fws_df)),
bootstrap_options = c("striped", "hover"))
| |
decade |
taxonomic_group |
current_status |
species_count |
| 141 |
2010s |
Mammals |
Resolved Taxon |
1 |
| 142 |
2010s |
Mammals |
Threatened |
11 |
| 143 |
2010s |
Reptiles |
Endangered |
4 |
| 144 |
2010s |
Reptiles |
Threatened |
9 |
| 145 |
2010s |
Snails |
Endangered |
18 |
| 146 |
2010s |
Snails |
Threatened |
1 |
par(mfrow=c(5,3), mar=c(5, 5, 2, 1))
output <- by(fws_df, fws_df$taxonomic_group, function(sub) {
plot_mat <- with(sub, tapply(species_count, list(decade, current_status), sum))
barplot(plot_mat, main=sub$taxonomic_group[[1]], cex.main=1.5,
ylim = c(0, max(plot_mat, na.rm=TRUE)+5),
col=seaborn_palette[seq_along(row.names(plot_mat))], beside=TRUE)
legend("top", row.names(plot_mat), fill=seaborn_palette[seq_along(row.names(plot_mat))],
ncol=length(row.names(plot_mat)))
box()
})

U.S. Department of Agriculture: Agriculture Census
sql <- "SELECT year,
CASE
WHEN domain_category = '' THEN 'TOTAL\nOPERATIONS'
ELSE REPLACE(REPLACE(REPLACE(domain_category, '(', '\n('), 'TO', 'TO\n'), 'OR', 'OR\n')
END AS domain_category,
value
FROM ag_census
WHERE data_item = 'FARM OPERATIONS - NUMBER OF OPERATIONS'"
agcensus_df <- dbGetQuery(conn, sql)
plot_mat <- with(agcensus_df, tapply(value, list(year, domain_category), sum))
par(mar=c(5, 5, 2, 1))
barplot(plot_mat, main="U.S. Agriculture Census: Number of Farm Operations", cex.main=1.5,
col=seaborn_palette[1:5], ylim=c(0, 2E6+5E5), beside=TRUE, xaxt="n", yaxt="n")
axis(side=1, at=c(3,9,15,21,27,33,39,45)+0.5, labels=colnames(plot_mat), tick=FALSE, padj=0.9, pos=c(0,0))
axis(side=2, at=axTicks(2), labels=format(axTicks(2), big.mark=',', scientific=FALSE), las=1)
legend("top", row.names(plot_mat), fill=seaborn_palette[1:5], ncol=5)

sql <- "WITH sub AS (
SELECT year,
data_item,
REPLACE(
REPLACE(
REPLACE(
REPLACE(
REPLACE(domain_category, ';', ','),
'(', '\n('),
'TO', 'TO\n'),
' OR', ' OR\n'),
'LESS', 'LESS\n') AS domain_category,
value
FROM ag_census
WHERE data_item LIKE '%COMMODITY TOTALS%'
AND value IS NOT NULL
)
SELECT year,
CASE
WHEN TRIM(domain_category) = ''
THEN CASE
WHEN data_item = 'COMMODITY TOTALS - SALES; MEASURED IN $'
THEN 'TOTAL\nSALES'
WHEN data_item = 'COMMODITY TOTALS - SALES; MEASURED IN $ / OPERATION'
THEN 'TOTAL\nSALES\nPER OPERATION'
END
ELSE domain_category
END AS domain_category,
value
FROM sub
"
agcensus_df <- within(dbGetQuery(conn, sql),
domain_category <- factor(domain_category,
levels = c("FARM SALES: \n(LESS\n THAN 2,500 $)", "FARM SALES: \n(2,500 TO\n 4,999 $)",
"FARM SALES: \n(5,000 TO\n 9,999 $)", "FARM SALES: \n(10,000 TO\n 24,999 $)",
"FARM SALES: \n(25,000 TO\n 49,999 $)", "FARM SALES: \n(50,000 TO\n 99,999 $)",
"FARM SALES: \n(100,000 TO\n 499,999 $)", "FARM SALES: \n(500,000 OR\n MORE $)",
"TOTAL\nSALES", "TOTAL\nSALES\nPER OPERATION"))
)
kable_styling(kable(tail(agcensus_df)),
bootstrap_options = c("striped", "hover"))
| |
year |
domain_category |
value |
| 45 |
2017 |
FARM SALES:
(5,000 TO
9,999 $) |
208074 |
| 46 |
2017 |
FARM SALES:
(10,000 TO
24,999 $) |
228218 |
| 47 |
2017 |
FARM SALES:
(25,000 TO
49,999 $) |
144113 |
| 48 |
2017 |
FARM SALES:
(50,000 TO
99,999 $) |
119434 |
| 49 |
2017 |
FARM SALES:
(100,000 TO
499,999 $) |
218771 |
| 50 |
2017 |
FARM SALES:
(500,000 OR
MORE $) |
146568 |
plot_mat <- with(agcensus_df[agcensus_df$domain_category != 'TOTAL\nSALES',],
tapply(value, list(year, factor(domain_category)), sum))
par(mar=c(5, 5, 2, 1))
barplot(plot_mat, main="U.S. Agriculture Census: Commodity Totals - Farm Sales", cex.main=1.5,
col=seaborn_palette[1:5], ylim=c(0, 1E6), beside=TRUE, xaxt="n", yaxt="n")
axis(side=1, at=c(3,9,15,21,27,33,39,45,51)+0.5, labels=colnames(plot_mat), tick=FALSE, padj=0.9, pos=c(0,0))
axis(side=2, at=axTicks(2), labels=format(axTicks(2), big.mark=',', scientific=FALSE), las=1)
legend("top", row.names(plot_mat), fill=seaborn_palette[1:5], ncol=5)

plot_mat <- with(agcensus_df[agcensus_df$domain_category == 'TOTAL\nSALES',],
tapply(value, list(year, factor(domain_category)), sum))
par(mar=c(5, 8, 2, 1))
barplot(plot_mat, main="U.S. Agriculture Census: Commodity Totals - Overall Farm Sales", cex.main=1.5,
col=seaborn_palette[1:5], ylim=c(0, 4E11+5E10), beside=TRUE, xaxt="n", yaxt="n", space=2)
axis(side=1, at=c(2.5,5.5,8.5,11.5,14.5), labels=row.names(plot_mat), padj=0.9, pos=c(0,0))
axis(side=2, at=axTicks(2), labels=format(axTicks(2), big.mark=',', scientific=FALSE), las=1)
legend("top", row.names(plot_mat), fill=seaborn_palette[1:5], ncol=5)

sql <- "SELECT year,
REPLACE(REPLACE(data_item, ' - OPERATIONS WITH AREA HARVESTED', ''), '; GRAIN', '') as crop,
value
FROM ag_census
WHERE data_item IN ('CORN; GRAIN - OPERATIONS WITH AREA HARVESTED',
'WHEAT - OPERATIONS WITH AREA HARVESTED',
'SOYBEANS - OPERATIONS WITH AREA HARVESTED')
ORDER BY year, data_item
"
crops_df <- dbGetQuery(conn, sql)
kable_styling(kable(tail(crops_df)),
bootstrap_options = c("striped", "hover"))
| |
year |
crop |
value |
| 10 |
2012 |
CORN |
348530 |
| 11 |
2012 |
SOYBEANS |
302963 |
| 12 |
2012 |
WHEAT |
147632 |
| 13 |
2017 |
CORN |
304801 |
| 14 |
2017 |
SOYBEANS |
303191 |
| 15 |
2017 |
WHEAT |
104792 |
plot_mat <- with(crops_df, tapply(value,
list(year, factor(crop, levels=c("WHEAT", "SOYBEANS", "CORN"))), sum)
)
par(mar=c(5, 8, 2, 1))
barplot(plot_mat, main="U.S. Agriculture Census: Number of Operations by Specific Crops", cex.main=1.5,
col=seaborn_palette[1:5], ylim=c(0, 5E5), yaxt="n", beside=TRUE)
axis(side=2, at=axTicks(2), labels=format(axTicks(2), big.mark=',', scientific=FALSE), las=1)
legend("top", row.names(plot_mat), fill=seaborn_palette[1:5], ncol=5)

sql <- "SELECT year,
REPLACE(
REPLACE(
REPLACE(data_item, ' - OPERATIONS WITH SALES', ''),
'; INCL CALVES', ''),
'; BROILERS', '') as livestock,
value
FROM ag_census
WHERE data_item IN ('CATTLE; INCL CALVES - OPERATIONS WITH SALES',
'HOGS - OPERATIONS WITH SALES',
'CHICKENS; BROILERS - OPERATIONS WITH SALES')
ORDER BY year, data_item
"
livestock_df <- dbGetQuery(conn, sql)
kable_styling(kable(tail(livestock_df)),
bootstrap_options = c("striped", "hover"))
| |
year |
livestock |
value |
| 10 |
2012 |
CATTLE |
740978 |
| 11 |
2012 |
CHICKENS |
32935 |
| 12 |
2012 |
HOGS |
55882 |
| 13 |
2017 |
CATTLE |
711827 |
| 14 |
2017 |
CHICKENS |
32751 |
| 15 |
2017 |
HOGS |
64871 |
plot_mat <- with(livestock_df, tapply(value,
list(year, factor(livestock, levels=c( "CHICKENS", "HOGS", "CATTLE"))), sum)
)
par(mar=c(5, 8, 2, 1))
barplot(plot_mat, main="U.S. Agriculture Census: Number of Operations by Specific Livestock", cex.main=1.5,
col=seaborn_palette[1:5], ylim=c(0, 1E6+2E5), yaxt="n", beside=TRUE)
axis(side=2, at=axTicks(2), labels=format(axTicks(2), big.mark=',', scientific=FALSE), las=1)
legend("top", row.names(plot_mat), fill=seaborn_palette[1:5], ncol=5)

sql <- "SELECT year,
CASE
WHEN data_item = 'CROP TOTALS - SALES; MEASURED IN $' THEN 'CROP TOTALS'
WHEN data_item = 'ANIMAL TOTALS; INCL PRODUCTS - SALES; MEASURED IN $' THEN 'ANIMAL TOTALS'
END as category,
value
FROM ag_census
WHERE data_item IN ('CROP TOTALS - SALES; MEASURED IN $',
'ANIMAL TOTALS; INCL PRODUCTS - SALES; MEASURED IN $')
ORDER BY year, data_item
"
agtotal_df <- dbGetQuery(conn, sql)
kable_styling(kable(tail(agtotal_df)),
bootstrap_options = c("striped", "hover"))
| |
year |
category |
value |
| 5 |
2007 |
ANIMAL TOTALS |
153562563000 |
| 6 |
2007 |
CROP TOTALS |
143657928000 |
| 7 |
2012 |
ANIMAL TOTALS |
182247407000 |
| 8 |
2012 |
CROP TOTALS |
212397074000 |
| 9 |
2017 |
ANIMAL TOTALS |
194975996000 |
| 10 |
2017 |
CROP TOTALS |
193546699000 |
plot_mat <- with(agtotal_df, tapply(value, list(year, category), sum))
par(mar=c(5, 8, 2, 1))
barplot(plot_mat, main="U.S. Agriculture Census: Crop vs Animal Sales", cex.main=1.5,
col=seaborn_palette[1:5], ylim=c(0, 2E11+5E10), yaxt="n", beside=TRUE)
axis(side=2, at=axTicks(2), labels=format(axTicks(2), big.mark=',', scientific=FALSE), las=1)
legend("top", row.names(plot_mat), fill=seaborn_palette[1:5], ncol=5)

World Metrics
sql <- "WITH pop AS
(SELECT p.year,
p.population
FROM world_population p
WHERE p.country_name = 'World'
AND p.year BETWEEN 2000 AND 2019
),
land AS
(SELECT a.year,
a.percent_arable
FROM arable_land a
WHERE a.country_name = 'World'
AND a.year BETWEEN 2000 AND 2019
),
fauna AS
(SELECT i.year,
SUM(i.species_count) AS animals_count
FROM iucn_species_count i
WHERE i.year BETWEEN 2000 AND 2019
GROUP BY i.year
),
flora AS
(SELECT p.assessment_year as year,
COUNT(*) AS plants_count
FROM plants_assessments p
WHERE p.assessment_year BETWEEN 2000 AND 2019
AND p.interpreted_status = 'Threatened'
GROUP BY p.assessment_year
),
ice AS
(SELECT s.date_year as year,
AVG(s.extent) FILTER(WHERE s.region = 'Arctic') AS arctic_sea_ice_extent,
AVG(s.extent) FILTER(WHERE s.region = 'Antarctica') AS antarctic_sea_ice_extent
FROM sea_ice_extent s
WHERE s.date_year BETWEEN 2000 AND 2019
GROUP BY s.date_year
),
ocean AS
(SELECT o.year as year,
AVG(o.tco2) AS total_co2,
AVG(o.phts25p0) AS ph_scale
FROM ocean_data o
WHERE o.year BETWEEN 2000 AND 2019
AND o.tco2 <> -9999 AND o.phts25p0 <> -9999
GROUP BY o.year
),
temp AS
(SELECT g.year as year,
AVG(g.global_mean) AS global_mean
FROM global_temperature g
WHERE g.year BETWEEN 2000 AND 2019
GROUP BY g.year
)
SELECT pop.year, pop.population, land.percent_arable, fauna.animals_count, flora.plants_count,
ice.arctic_sea_ice_extent, ice.antarctic_sea_ice_extent,
ocean.total_co2, ocean.ph_scale, temp.global_mean
FROM pop
JOIN land USING (year)
JOIN fauna USING (year)
JOIN flora USING (year)
JOIN ice USING (year)
JOIN ocean USING (year)
JOIN temp USING (year)
ORDER BY pop.year"
env_world_df <- dbGetQuery(conn, sql)
kable_styling(kable(tail(env_world_df)), font_size = 12,
bootstrap_options = c("striped", "hover"))
| |
year |
population |
percent_arable |
animals_count |
plants_count |
arctic_sea_ice_extent |
antarctic_sea_ice_extent |
total_co2 |
ph_scale |
global_mean |
| 7 |
2008 |
6765986891 |
10.80640 |
16928 |
3279 |
10.97785 |
12.23941 |
2181.326 |
7.685694 |
0.5155556 |
| 8 |
2009 |
6849272706 |
10.77478 |
17291 |
2294 |
10.93197 |
12.04859 |
2176.104 |
7.735574 |
0.6294444 |
| 9 |
2010 |
6932596129 |
10.74432 |
18351 |
3217 |
10.71139 |
12.10679 |
2191.072 |
7.702093 |
0.7022222 |
| 10 |
2011 |
7014792135 |
10.80189 |
19570 |
2850 |
10.48350 |
11.50057 |
2224.690 |
7.675097 |
0.5844444 |
| 11 |
2012 |
7099311892 |
10.87358 |
20219 |
7556 |
10.40610 |
12.00444 |
2187.112 |
7.731700 |
0.6183333 |
| 12 |
2013 |
7184861447 |
10.89469 |
21353 |
5465 |
10.89712 |
12.52361 |
2183.400 |
7.723483 |
0.6438889 |
kable_styling(kable(cor(env_world_df[-1])), font_size = 12,
bootstrap_options = c("striped", "hover"))
| |
population |
percent_arable |
animals_count |
plants_count |
arctic_sea_ice_extent |
antarctic_sea_ice_extent |
total_co2 |
ph_scale |
global_mean |
| population |
1.0000000 |
0.1656147 |
0.9774118 |
0.7453433 |
-0.8026257 |
0.5047082 |
0.5832658 |
-0.1705909 |
0.5630321 |
| percent_arable |
0.1656147 |
1.0000000 |
0.2441270 |
0.4917138 |
0.0689147 |
0.3901610 |
-0.0431136 |
0.1567368 |
-0.3399306 |
| animals_count |
0.9774118 |
0.2441270 |
1.0000000 |
0.7450838 |
-0.8024194 |
0.5181381 |
0.6740957 |
-0.2912376 |
0.4783654 |
| plants_count |
0.7453433 |
0.4917138 |
0.7450838 |
1.0000000 |
-0.7224389 |
0.3576609 |
0.3673134 |
-0.1134181 |
0.3630865 |
| arctic_sea_ice_extent |
-0.8026257 |
0.0689147 |
-0.8024194 |
-0.7224389 |
1.0000000 |
-0.0588615 |
-0.7926131 |
0.5220906 |
-0.5461557 |
| antarctic_sea_ice_extent |
0.5047082 |
0.3901610 |
0.5181381 |
0.3576609 |
-0.0588615 |
1.0000000 |
0.1042700 |
0.0767117 |
0.0713994 |
| total_co2 |
0.5832658 |
-0.0431136 |
0.6740957 |
0.3673134 |
-0.7926131 |
0.1042700 |
1.0000000 |
-0.8165837 |
0.2653096 |
| ph_scale |
-0.1705909 |
0.1567368 |
-0.2912376 |
-0.1134181 |
0.5220906 |
0.0767117 |
-0.8165837 |
1.0000000 |
0.0463203 |
| global_mean |
0.5630321 |
-0.3399306 |
0.4783654 |
0.3630865 |
-0.5461557 |
0.0713994 |
0.2653096 |
0.0463203 |
1.0000000 |
par(mfrow=c(4,2), mai = c(0.3, 0.2, 0.7, 0.2))
for(x in colnames(env_world_df)[3:ncol(env_world_df)]) {
lfit <- loess(paste(x, "~ population"), data=env_world_df)
plot(as.formula(paste(x, "~ population")), env_world_df,
main=paste("population and\n", gsub("_", " ", x)), cex.main=2,
type="p", col=seaborn_palette[1], yaxt='n', xaxt='n', pch=19)
axis(side=1, at=axTicks(1), labels=format(axTicks(1), big.mark=',', scientific=FALSE))
axis(side=2, at=axTicks(2), labels=format(axTicks(2), big.mark=',', scientific=FALSE))
pop_order <- order(env_world_df$population)
lines(env_world_df$population[pop_order], lfit$fitted[pop_order], col=seaborn_palette[4], lwd=3)
}

dbDisconnect(conn)
[1] TRUE
User Data Application
# LINUX SHELL COMMAND CALL
system(paste0("gnome-terminal -- Rscript -e \"library(shiny); setwd('", getwd(), "'); runApp('EnvironmentDB_Shiny_App.R')\""))
Conclusion
Postgres as tool in data science workflow
Data persistence and hygiene
Centralization and security
Efficiency and usefulness
Scalability and applicability
---
title: "PostgreSQL As Data Science Database"
output: html_notebook
---

<style type="text/css">
.main-container {
  max-width: 1000px;
  margin-left: auto;
  margin-right: auto;
}
</style>

<br/>
<div style="float:left"><img src="IMAGES/postgresql_r.png" width="200px"/></div>

```{r setup, include=FALSE}
knitr::opts_chunk$set(root.dir = getwd())
```

```{r}
suppressMessages(library(kableExtra))
suppressMessages(library(xts))

seaborn_palette <- c("#4C72B0", "#DD8452", "#55A868", "#C44E52", "#8172B3", "#937860", 
                     "#DA8BC3", "#8C8C8C", "#CCB974", "#64B5CD", "#4C72B0", "#DD8452")
```

<div style="float:right"><img src="IMAGES/open_source.png" width="75px"/></div>
<br/>

## Open Source Success Stories

<br/>

<div style="float:right"><img src="IMAGES/postgresql.jpeg" width="50px"/></div>
- ### PostgreSQL: powerful, extensible, analytical RDBMS
    - #### [Community](https://www.postgresql.org/community/): contributors, mailing lists, local user groups
<div style="float:right"><img src="IMAGES/r_logo.png" width="50px"/></div>
- ### R: data science stack, extensible environment
    - #### [CRAN](https://cran.r-project.org/) repository of packages
<div style="float:right"><img src="IMAGES/python.png" width="50px"/></div>
- ### Python: general purpose, extensible language
    - #### [PyPI](https://pypi.org/) ecosystem of modules

<div style="margin-left:40px;"/><h3>Challenging Commercial Market Share</h3></div>
<div style="float:left; margin-left:100px;"/><img src="IMAGES/commercial_tools.png" width="200px"/></div>
<br/>
<br/>
<br/>
<br/>

<hr style="border: none; height: 1px; background-color: #CCC;"/>

<br/>

<h2><span style="color: #336791"/>Relational Database Solution</span></h2>

<div style="float:right;"><img src="IMAGES/RDBMS_Group.png" width="250px"/></div>

- ### <span style="color: #336791"/>Data persistence: historical and current needs</span>
- ### <span style="color: #336791"/>Maintenance/hygiene: adherence to types</span>

<div style="float:right;"><img src="IMAGES/postgresql.jpeg" width="100px"/></div>

- ### <span style="color: #336791"/>Storage efficiency: normalization reduces repetition of data</span>
- ### <span style="color: #336791"/>Centralization: multiple user environment and security</span>
- ### <span style="color: #336791"/>Scalability: not limited to local resources</span>

<hr style="border: none; height: 1px; background-color: #CCC;"/>

<div style="float:right"><img src="IMAGES/docker_pgsql.png" width="150px"/></div>
## <span style="color: #336791"/>Use Case: Environment Database</a></span>

<h3><span style="color: #336791"/><i>Human Impact on the Global and Local Biosphere and Climate</i></span></h3>
<div style="float:center; text-align:center;"><img src="IMAGES/env_data.png"/></div>
<br/>

<hr style="border: none; height: 1px; background-color: #CCC;"/>

## Connection

- ### No file system of folders and subfolders
- ### No multiple flat files
- ### Centralized location for multiple users

### <span style="color: #336791"/><a href="https://www.postgresql.org/ftp/odbc/versions/src/" target="blank">ODBC</a></span>

```{r}
library(DBI)
library(odbc)

conn <- dbConnect(odbc::odbc(), driver="PostgreSQL Unicode", 
                  server="localhost", database="environment",
                  uid="postgres", pwd="env19", port=6432)
dbListTables(conn)

dbDisconnect(conn)
```

### <span style="color: #336791"/><a href="https://jdbc.postgresql.org/download.html" target="blank">JDBC</a></span>

```{r}
library(RJDBC)

drv <- JDBC("org.postgresql.Driver",
           "/usr/lib/jvm/java-8-oracle/lib/postgresql-42.2.2.jar")
conn <- dbConnect(drv, "jdbc:postgresql://localhost:6432/environment", "postgres", "env19")
dbListTables(conn, schema="public")

dbDisconnect(conn)
```


### <span style="color: #336791"/><a href="https://rdrr.io/cran/RPostgreSQL/" target="blank">R-Postgres API</a></span>

```{r}
library(RPostgreSQL)

conn <- dbConnect(RPostgreSQL::PostgreSQL(), host="localhost", dbname="environment",
                  user="postgres", password="env19", port=6432)
dbListTables(conn)
```

<hr style="border: none; height: 1px; background-color: #CCC;"/>

## Process Data

- #### Proactively define data types and scale
- #### Seamless bulk text file import
- #### Annotate sources with comments

```{sql, eval=FALSE}
CREATE TABLE global_temperature (
   id SERIAL NOT NULL PRIMARY KEY,
   year INT,
   period VARCHAR(50),
   global_mean NUMERIC(5,2)
);

\copy global_temperature (year, period, global_mean) FROM '/home/parfaitg/Databases/SQL_Server/ENVIRONMENT/global_temperature.csv' DELIMITER ',' CSV HEADER;

COMMENT ON TABLE global_temperature IS 'Source: NASA - Combined Land-Surface Air and Sea-Surface Water Temperature Anomalies (Land-Ocean Temperature Index, LOTI) (https://data.giss.nasa.gov/gistemp/)';
```

```{sql connection=conn, output.var = "comments_df"}
SELECT relname as table, obj_description(oid) as comment
FROM pg_class
WHERE relkind = 'r'
  AND obj_description(oid) IS NOT NULL
  ORDER BY relname
```

```{r}
kable_styling(kable(comments_df),
              bootstrap_options = c("striped", "hover"))
```

<hr style="border: none; height: 1px; background-color: #CCC;"/>

## Analyze Data

- #### SQL joins disparate datasets
- #### Queries expose relationship
- #### Adjust columns for table/graph needs

<div style="float:right"><a href="https://www.esrl.noaa.gov/gmd/obop/mlo/" blank="target"><img src="IMAGES/noaa.png" height="50px"/></a></div>
<div style="float:right"><a href="https://www.eia.gov/totalenergy/data/monthly/" blank ="taget"><img src="IMAGES/doe_eia.png" height="50px"/></a></div>


### <span style="color: #336791"/>Department of Energy's EIA: Monthly Energy Review &</span>
### <span style="color: #336791"/>NOAA Mauna Loa, Hawaii Observatory - Carbon PPM</span>

```{sql connection=conn, output.var = "metrics_df"}
SELECT p.date_year, p.date_month, CONCAT(p.date_year, '-', p.date_month, '-01')::date as date_day,
               p.average_ppm as "carbon ppm", c.energy_consumed as "btu consumed", e.energy_co2 as "co2 emissions"
  FROM ppm_month p
  JOIN consumption c 
    ON p.date_year = c.date_year AND p.date_month = c.date_month and c.msn = 'TXRCBUS'
  JOIN us_co2_emissions e
    ON p.date_year = e.date_year AND p.date_month = e.date_month and e.msn = 'TETCEUS'
  ORDER BY p.date_year, p.date_month
```

```{sql connection=conn, output.var = "agg_df"}
SELECT p.date_year,
       sum(p.average_ppm) as carbon_ppm_total, 
       avg(p.average_ppm) as carbon_ppm_mean, 
       sum(c.energy_consumed) as btu_consumed_total,
       avg(c.energy_consumed) as btu_consumed_mean,
       sum(e.energy_co2) as co2_emissions_total,
       avg(e.energy_co2) as co2_emissions_mean
  FROM ppm_month p
  JOIN consumption c 
    ON p.date_year = c.date_year AND p.date_month = c.date_month and c.msn = 'TXRCBUS'
  JOIN us_co2_emissions e
    ON p.date_year = e.date_year AND p.date_month = e.date_month and e.msn = 'TETCEUS'
GROUP BY p.date_year
ORDER BY p.date_year
```

```{r}
kable_styling(kable(tail(agg_df, 10)),
              bootstrap_options = c("striped", "hover"))
```

```{r fig1a, fig.height = 7, fig.width = 13, fig.align = "center"}
metric_ts <- xts(x=metrics_df[c("carbon ppm", "btu consumed", "co2 emissions")], 
                 order.by=metrics_df$date_day)

plot(metric_ts, main = "Energy and Carbon PPM Metrics",
     legend.loc="bottomright", 
     col = seaborn_palette[1:3],
     yaxis.right=FALSE,
     axes=FALSE,
     lwd=1,
     cex.main=3,
     major.ticks="years",
     major.format="%Y",
     minor.format="%Y",
     grid.ticks.lty=1)
```

### <span style="color: #336791"/>Seasonal Decomposition</span>

```{r  fig1b, fig.height = 7, fig.width = 13, fig.align = "center"}
carbonppm_ts <- ts(metrics_df$`carbon ppm`, start=c(1973, 1), frequency=12)
carbonppm_stl <- stl(carbonppm_ts, s.window="periodic")

plot(carbonppm_stl, col = seaborn_palette[1], 
     main="Seasonal Decomposition of Global Carbon PPM")
```

```{r  fig1c, fig.height = 7, fig.width = 13, fig.align = "center"}
consumed_ts <- ts(metrics_df$`btu consumed`, start=c(1973, 1), frequency=12)
consumed_stl <- stl(consumed_ts, s.window="periodic")

plot(consumed_stl, col = seaborn_palette[2], 
     main="Seasonal Decomposition of U.S. Energy Consumption")
```

```{r fig1d, fig.height = 7, fig.width = 13, fig.align = "center"}
emissions_ts <- ts(metrics_df$`co2 emissions`, start=c(1973, 1), frequency=12)
emissions_stl <- stl(emissions_ts, s.window="periodic")

plot(emissions_stl, col = seaborn_palette[3],
     main = "Seasonal Decomposition of U.S. CO2 Emissions")
```

```{r}
sql <- "WITH c1 AS
           (SELECT CONCAT((date_year/10)::int * 10, 's') as decade, 
                   REPLACE(description, 'Primary Energy Consumed by the ', '') AS \"Sector\",
                   SUM(energy_consumed) AS \"Primary Energy Consumed\"
            FROM consumption
            WHERE msn IN ('TXICBUS', 'TXRCBUS', 'TXACBUS', 'TXCCBUS', 'TXEIBUS')
            GROUP BY CONCAT((date_year/10)::int * 10, 's'), description
           ), c2 AS
           (SELECT CONCAT((date_year/10)::int * 10, 's') as decade, 
                   REPLACE(description, 'Total Energy Consumed by the ', '') AS \"Sector\",
                   SUM(energy_consumed) AS \"Total Energy Consumed\"
            FROM consumption
            WHERE msn IN ('TECCBUS', 'TEACBUS', 'TEICBUS', 'TERCBUS')
            GROUP BY CONCAT((date_year/10)::int * 10, 's'), description
           )

        SELECT c1.decade, c1.\"Sector\", c1.\"Primary Energy Consumed\", c2.\"Total Energy Consumed\"
        FROM c1
        LEFT JOIN c2
            ON c1.\"Sector\" = c2.\"Sector\" AND c1.decade = c2.decade
        ORDER BY c1.decade, c1.\"Sector\"
       "

consumed_df <- dbGetQuery(conn, sql)

kable_styling(kable(tail(consumed_df)),
              bootstrap_options = c("striped", "hover"))
```

<div style="float:right"><img src="IMAGES/doe_eia.png" width="75px"/></div>
### <span style="color: #336791"/>Consumption and CO2 Emissions</span>


```{r fig1e, fig.height = 6, fig.width = 15, fig.align = "center"}
plot_mat <- with(subset(consumed_df, Sector != 'Electric Power Sector'),
                 tapply(`Total Energy Consumed`, list(decade, `Sector`), sum))

par(mar=c(5, 5, 2, 1))
barplot(plot_mat, main="U.S. Total Energy Consumption by Sector", cex.main=1.5,
        col=seaborn_palette[1:8], ylim=c(0, 8E5), xaxt="n", yaxt="n", beside=TRUE)

axis(side=1, at=c(5, 14, 23, 32), labels=colnames(plot_mat), padj=0.9, pos=c(0,0))
axis(side=2, at=axTicks(2), labels=format(axTicks(2), big.mark=',', scientific=FALSE), las=1)
legend("top", row.names(plot_mat), fill=seaborn_palette[1:8], ncol=8)
```

```{r fig1f, fig.height = 6, fig.width = 15, fig.align = "center"}
plot_mat <- with(consumed_df, tapply(`Primary Energy Consumed`, list(decade, `Sector`), sum))

par(mar=c(5, 5, 2, 1))
barplot(plot_mat, main="U.S. Primary Energy Consumption by Sector", cex.main=1.5,
        col=seaborn_palette[1:8], ylim=c(0, 9E5), xaxt="n", yaxt="n", beside=TRUE)

axis(side=1, at=c(5, 14, 23, 32, 41), labels=colnames(plot_mat), padj=0.9, pos=c(0,0))
axis(side=2, at=axTicks(2), labels=format(axTicks(2), big.mark=',', scientific=FALSE), las=1)
legend("top", row.names(plot_mat), fill=seaborn_palette[1:8], ncol=8)
```

```{r}
sql <- "SELECT CONCAT((date_year/10)::int * 10, 's') as decade, 
               REPLACE(REPLACE(description, 'Total Energy ', ''), ' CO2 Emissions', '') AS \"Sector\",
               SUM(energy_co2) AS \"Total CO2 Emissions\"
        FROM us_co2_emissions
        WHERE msn IN ('TERCEUS', 'TECCEUS', 'TEACEUS', 'TXEIEUS')
        GROUP BY date_year, description
        ORDER BY date_year, description
       "

emissions_df <- dbGetQuery(conn, sql)

kable_styling(kable(tail(emissions_df)),
              bootstrap_options = c("striped", "hover"))
```

```{r fig1g, fig.height = 6, fig.width = 15, fig.align = "center"}
plot_mat <- with(emissions_df, tapply(`Total CO2 Emissions`, list(decade, `Sector`), sum))

par(mar=c(5, 8, 2, 1))
barplot(plot_mat, main="U.S. CO2 Emissions by Sector", cex.main=1.5,
        col=seaborn_palette[1:5], ylim=c(0, 6E4), xaxt="n", yaxt="n", beside=TRUE)

axis(side=1, at=c(3, 9, 15, 21)+0.5, labels=colnames(plot_mat), padj=0.9, pos=c(0,0))
axis(side=2, at=axTicks(2), labels=format(axTicks(2), big.mark=',', scientific=FALSE), las=1)
legend("top", row.names(plot_mat), fill=seaborn_palette[1:5], ncol=5)
```


```{r}
sql <- "WITH sub AS
          (SELECT CONCAT((date_year/10)::int * 10, 's') as decade, 
                  REPLACE($1, '%', '') as sector,
                  REPLACE(
                     REPLACE(
                       REPLACE(
                               REPLACE(description, 'Commercial Sector CO2 Emissions', ''), 
                               'Residential Sector CO2 Emissions', ''
                       ), 'Transportation Sector CO2 Emissions', ''),
                  ' ', '\n')  AS \"Type\",
                 energy_co2
           FROM us_co2_emissions
           WHERE description LIKE $2)

       SELECT decade, sector, \"Type\", 
              SUM(energy_co2) AS \"Total CO2 Emissions\"
       FROM sub
       GROUP BY decade, sector, \"Type\"
       ORDER BY decade, sector, \"Type\"
       "

params <- paste0(c("%Transportation", "%Residential", "%Commercial"), " Sector%")
emissions_type_df <- do.call(rbind, lapply(params, function(p) dbGetQuery(conn, sql, param=list(p, p))))
```

```{r fig1h, fig.height = 12, fig.width = 15, fig.align = "center"}

par(mfrow=c(3,2), mar=c(5, 5, 2, 1), mai = c(0.7, 0.2, 0.7, 0.2))
output <- by(emissions_type_df, emissions_type_df$sector, function(sub) {
  plot_mat <- with(sub, tapply(`Total CO2 Emissions`, list(decade, `Type`), sum))

  barplot(plot_mat, main=paste("U.S. CO2 Emissions by", sub$sector[[1]]), cex.main=1.5,
          col=seaborn_palette[1:5], ylim=c(0, ceiling(max(plot_mat, na.rm=TRUE)/1E4) * 1E4), 
          xaxt="n", yaxt="n", beside=TRUE)
  
  axis(side=1, at=c(3,9,15,21,27,33,39,45,51,57,63)[seq_along(colnames(plot_mat))] + 0.5,
       labels=colnames(plot_mat), padj=0.9, pos=c(0,0))
  axis(side=2, at=axTicks(2), labels=format(axTicks(2), big.mark=',', scientific=FALSE), las=1)
  legend("top", row.names(plot_mat), fill=seaborn_palette[1:5], ncol=5)
})
```

<div style="float:right"><img src="IMAGES/doe_eia.png" width="75px"/></div>
### <span style="color: #336791"/>Renewable Energy: Production and Consumption</span>

```{r}
sql <- "SELECT energy_type,
               date,
               SUM(production) AS production,
               SUM(consumption) AS consumption
        FROM us_renewable_energy
        GROUP BY energy_type,
                 date
        ORDER BY energy_type,
                 date
       "

renewable_df <- dbGetQuery(conn, sql)

kable_styling(kable(tail(renewable_df)),
              bootstrap_options = c("striped", "hover"))
```

```{r renewable_fig, fig.height = 15, fig.width = 15, fig.align = "center"}

par(mfrow=c(3,3), mar=c(5, 5, 2, 1))
output <- by(renewable_df, renewable_df$energy_type, function(sub) {
  
  metric_ts <- xts(x=sub[c("production", "consumption")], 
                   order.by=sub$date)
  
  print(plot(metric_ts, main = sub$energy_type[1],
             legend.loc="bottomright", 
             col = seaborn_palette[1:3],
             yaxis.right=FALSE,
             axes=FALSE,
             lwd=1,
             cex.main=3,
             major.ticks="years",
             major.format="%Y",
             minor.format="%Y",
             grid.ticks.lty=1))
  
})
```

<div style="float:right"><a href="https://waterdata.usgs.gov/nwis/gw"><img src="IMAGES/usgs.png" width="75px"/></a></div>

### <span style="color: #336791"/>U.S. Geological Survey - Groundwater Well Depth Data</span>
```{r}
sql <- "SELECT g.year, g.month,
               avg(g.mean_value) as mean_value
        FROM groundwater g
        WHERE g.year BETWEEN 1990 AND 2019
        GROUP BY g.year, g.month"

groundwater_df <- dbGetQuery(conn, sql)

kable_styling(kable(tail(groundwater_df)),
              bootstrap_options = c("striped", "hover"))
```

```{r fig3, fig.height = 7, fig.width = 16, fig.align = "center"}
boxplot(mean_value ~ year, groundwater_df, col=seaborn_palette[1:10], 
        main="Groundwater Well Depth Mean Values", cex.main=1.5)
```


```{r}
sql <- "WITH sites AS (
            SELECT CONCAT(g.year, '-', g.month, '-1')::date AS \"date\",
                   g.site_name,
                   AVG(g.mean_value) as mean_value
            FROM groundwater g
            WHERE g.year BETWEEN 1990 AND 2019
            GROUP BY CONCAT(g.year, '-', g.month, '-1')::date,
                     g.site_name
        ), bottom AS (
            SELECT site_name, AVG(mean_value) AS mean_value, 'bottom_sites' AS category
            FROM sites 
            GROUP BY site_name
            ORDER BY AVG(mean_value) ASC LIMIT 5
        ), top AS (
            SELECT site_name, AVG(mean_value) AS mean_value, 'top_sites' AS category
            FROM sites 
            GROUP BY site_name
            ORDER BY AVG(mean_value) DESC LIMIT 5
        )

        SELECT sites.\"date\", sites.mean_value, sites.site_name, top.category
        FROM sites
        INNER JOIN top
            ON sites.site_name = top.site_name

        UNION

        SELECT sites.\"date\", sites.mean_value, sites.site_name, bottom.category
        FROM sites
        INNER JOIN bottom
            ON sites.site_name = bottom.site_name

        ORDER BY category, site_name, \"date\""

groundwater_df <- dbGetQuery(conn, sql)

kable_styling(kable(head(groundwater_df)),
              bootstrap_options = c("striped", "hover"))
```

```{r groundwater_fig, fig.height = 25, fig.width = 15, fig.align = "center"}

groundwater_df$year <- format(groundwater_df$date, "%Y")

par(mfrow=c(5,2), mar=c(5, 5, 2, 1))
output <- by(groundwater_df, groundwater_df$site_name, function(sub) {
  
  metric_ts <- xts(x=sub[c("mean_value")], 
                   order.by=sub$date)
  
  print(plot(metric_ts, main = paste(sub$site_name[1], ':', sub$category[1]),
             legend.loc="bottomright", 
             col = seaborn_palette[1:3],
             yaxis.right=FALSE,
             axes=FALSE,
             lwd=1,
             cex.main=3,
             major.ticks="years",
             major.format="%Y",
             minor.format="%Y",
             grid.ticks.lty=1))
  
})
```

<div style="float:right"><a href="https://www.fws.gov/endangered/" target="blank"><img src="IMAGES/us_fws.png" width="75px"/></a></div>
### U.S. FWS Endangered Species List

```{r}
sql <- "SELECT CONCAT((f.date_year/10)::int * 10, 's') AS decade, 
               f.taxonomic_group,
               f.current_status,
               count(*) AS species_count
        FROM fws_species_year f
        WHERE f.current_status IN ('Endangered', 'Recovery', 'Resolved Taxon', 
                                   'Threatened', 'Extinction')
        GROUP BY CONCAT((f.date_year/10)::int * 10, 's'),
                 f.taxonomic_group,
                 f.current_status
        ORDER BY CONCAT((f.date_year/10)::int * 10, 's'),
                 f.taxonomic_group,
                 f.current_status"

fws_df <- dbGetQuery(conn, sql)

kable_styling(kable(tail(fws_df)),
              bootstrap_options = c("striped", "hover"))
```

```{r fws_fig, fig.height = 15, fig.width = 15, fig.align = "center"}

par(mfrow=c(5,3), mar=c(5, 5, 2, 1))
output <- by(fws_df, fws_df$taxonomic_group, function(sub) {
  plot_mat <- with(sub, tapply(species_count, list(decade, current_status), sum))
  
  barplot(plot_mat, main=sub$taxonomic_group[[1]], cex.main=1.5,
          ylim = c(0, max(plot_mat, na.rm=TRUE)+5),
          col=seaborn_palette[seq_along(row.names(plot_mat))], beside=TRUE)
  legend("top", row.names(plot_mat), fill=seaborn_palette[seq_along(row.names(plot_mat))], 
         ncol=length(row.names(plot_mat)))
  box()
})
```


<div style="float:right"><a href="https://www.nass.usda.gov/AgCensus/" target="blank"><img src="IMAGES/usda.png" width="100px"/></a></div>
### <span style="color: #336791"/>U.S. Department of Agriculture: Agriculture Census</span>

```{r fig4, fig.height = 6, fig.width = 17, fig.align = "center"}
sql <- "SELECT year,
               CASE
                    WHEN domain_category = '' THEN 'TOTAL\nOPERATIONS'
                    ELSE REPLACE(REPLACE(REPLACE(domain_category, '(', '\n('), 'TO', 'TO\n'), 'OR', 'OR\n')
               END AS domain_category, 
               value
        FROM ag_census 
        WHERE data_item = 'FARM OPERATIONS - NUMBER OF OPERATIONS'"

agcensus_df <- dbGetQuery(conn, sql)

plot_mat <- with(agcensus_df, tapply(value, list(year, domain_category), sum))

par(mar=c(5, 5, 2, 1))
barplot(plot_mat, main="U.S. Agriculture Census: Number of Farm Operations", cex.main=1.5,
        col=seaborn_palette[1:5], ylim=c(0, 2E6+5E5), beside=TRUE, xaxt="n", yaxt="n")

axis(side=1, at=c(3,9,15,21,27,33,39,45)+0.5, labels=colnames(plot_mat), tick=FALSE, padj=0.9, pos=c(0,0))
axis(side=2, at=axTicks(2), labels=format(axTicks(2), big.mark=',', scientific=FALSE), las=1)
legend("top", row.names(plot_mat), fill=seaborn_palette[1:5], ncol=5)
```

```{r}
sql <- "WITH sub AS (
          SELECT year,
                 data_item,
                 REPLACE(
                    REPLACE(
                        REPLACE(
                           REPLACE(
                              REPLACE(domain_category, ';', ','),
                              '(', '\n('), 
                           'TO', 'TO\n'),
                        ' OR', ' OR\n'),
                    'LESS', 'LESS\n') AS domain_category, 
                 value
          FROM ag_census 
          WHERE data_item LIKE '%COMMODITY TOTALS%'
            AND value IS NOT NULL
       ) 

       SELECT year, 
              CASE
                  WHEN TRIM(domain_category) = '' 
                  THEN CASE 
                            WHEN data_item = 'COMMODITY TOTALS - SALES; MEASURED IN $' 
                            THEN 'TOTAL\nSALES'
                            WHEN data_item = 'COMMODITY TOTALS - SALES; MEASURED IN $ / OPERATION' 
                            THEN 'TOTAL\nSALES\nPER OPERATION'
                       END
                  ELSE domain_category 
              END AS domain_category,
              value
       FROM sub
      "

agcensus_df <- within(dbGetQuery(conn, sql),
                      domain_category <- factor(domain_category,
                                                levels = c("FARM SALES: \n(LESS\n THAN 2,500 $)", "FARM SALES: \n(2,500 TO\n 4,999 $)", 
                                                           "FARM SALES: \n(5,000 TO\n 9,999 $)", "FARM SALES: \n(10,000 TO\n 24,999 $)",
                                                           "FARM SALES: \n(25,000 TO\n 49,999 $)", "FARM SALES: \n(50,000 TO\n 99,999 $)",
                                                           "FARM SALES: \n(100,000 TO\n 499,999 $)", "FARM SALES: \n(500,000 OR\n MORE $)", 
                                                           "TOTAL\nSALES", "TOTAL\nSALES\nPER OPERATION"))
)

kable_styling(kable(tail(agcensus_df)),
              bootstrap_options = c("striped", "hover"))
```

```{r fig5, fig.height = 6, fig.width = 17, fig.align = "center"}
plot_mat <- with(agcensus_df[agcensus_df$domain_category != 'TOTAL\nSALES',], 
                 tapply(value, list(year, factor(domain_category)), sum))

par(mar=c(5, 5, 2, 1))
barplot(plot_mat, main="U.S. Agriculture Census: Commodity Totals - Farm Sales", cex.main=1.5,
        col=seaborn_palette[1:5], ylim=c(0, 1E6), beside=TRUE, xaxt="n", yaxt="n")

axis(side=1, at=c(3,9,15,21,27,33,39,45,51)+0.5, labels=colnames(plot_mat), tick=FALSE, padj=0.9, pos=c(0,0))
axis(side=2, at=axTicks(2), labels=format(axTicks(2), big.mark=',', scientific=FALSE), las=1)
legend("top", row.names(plot_mat), fill=seaborn_palette[1:5], ncol=5)
```


```{r fig6, fig.height = 6, fig.width = 15, fig.align = "center"}
plot_mat <- with(agcensus_df[agcensus_df$domain_category == 'TOTAL\nSALES',], 
                 tapply(value, list(year, factor(domain_category)), sum))

par(mar=c(5, 8, 2, 1))
barplot(plot_mat, main="U.S. Agriculture Census: Commodity Totals - Overall Farm Sales", cex.main=1.5,
        col=seaborn_palette[1:5], ylim=c(0, 4E11+5E10), beside=TRUE, xaxt="n", yaxt="n", space=2)
        
axis(side=1, at=c(2.5,5.5,8.5,11.5,14.5), labels=row.names(plot_mat), padj=0.9, pos=c(0,0))
axis(side=2, at=axTicks(2), labels=format(axTicks(2), big.mark=',', scientific=FALSE), las=1)
legend("top", row.names(plot_mat), fill=seaborn_palette[1:5], ncol=5)
```

```{r}
sql <- "SELECT year, 
               REPLACE(REPLACE(data_item, ' - OPERATIONS WITH AREA HARVESTED', ''), '; GRAIN', '') as crop,
               value
        FROM ag_census
        WHERE data_item IN ('CORN; GRAIN - OPERATIONS WITH AREA HARVESTED',
                            'WHEAT - OPERATIONS WITH AREA HARVESTED',
                            'SOYBEANS - OPERATIONS WITH AREA HARVESTED')
        ORDER BY year, data_item
       "

crops_df <- dbGetQuery(conn, sql)

kable_styling(kable(tail(crops_df)),
              bootstrap_options = c("striped", "hover"))
```

```{r fig7, fig.height = 6, fig.width = 15, fig.align = "center"}
plot_mat <- with(crops_df, tapply(value, 
                                  list(year, factor(crop, levels=c("WHEAT", "SOYBEANS", "CORN"))), sum)
)

par(mar=c(5, 8, 2, 1))
barplot(plot_mat, main="U.S. Agriculture Census: Number of Operations by Specific Crops", cex.main=1.5,
        col=seaborn_palette[1:5], ylim=c(0, 5E5), yaxt="n", beside=TRUE)

axis(side=2, at=axTicks(2), labels=format(axTicks(2), big.mark=',', scientific=FALSE), las=1)
legend("top", row.names(plot_mat), fill=seaborn_palette[1:5], ncol=5)
```

```{r}
sql <- "SELECT year, 
               REPLACE(
                  REPLACE(
                     REPLACE(data_item, ' - OPERATIONS WITH SALES', ''), 
                     '; INCL CALVES', ''),
                  '; BROILERS', '') as livestock,
               value
        FROM ag_census
        WHERE data_item IN ('CATTLE; INCL CALVES - OPERATIONS WITH SALES',
                            'HOGS - OPERATIONS WITH SALES',
                            'CHICKENS; BROILERS - OPERATIONS WITH SALES')
        ORDER BY year, data_item
       "

livestock_df <- dbGetQuery(conn, sql)

kable_styling(kable(tail(livestock_df)),
              bootstrap_options = c("striped", "hover"))
```


```{r fig8, fig.height = 6, fig.width = 15, fig.align = "center"}
plot_mat <- with(livestock_df, tapply(value, 
                                  list(year, factor(livestock, levels=c( "CHICKENS", "HOGS", "CATTLE"))), sum)
)

par(mar=c(5, 8, 2, 1))
barplot(plot_mat, main="U.S. Agriculture Census: Number of Operations by Specific Livestock", cex.main=1.5,
        col=seaborn_palette[1:5], ylim=c(0, 1E6+2E5), yaxt="n", beside=TRUE)

axis(side=2, at=axTicks(2), labels=format(axTicks(2), big.mark=',', scientific=FALSE), las=1)
legend("top", row.names(plot_mat), fill=seaborn_palette[1:5], ncol=5)
```


```{r}
sql <- "SELECT year, 
               CASE
                   WHEN data_item = 'CROP TOTALS - SALES; MEASURED IN $' THEN 'CROP TOTALS'
                   WHEN data_item = 'ANIMAL TOTALS; INCL PRODUCTS - SALES; MEASURED IN $' THEN 'ANIMAL TOTALS'
               END as category,
               value
        FROM ag_census
        WHERE data_item IN ('CROP TOTALS - SALES; MEASURED IN $',
                            'ANIMAL TOTALS; INCL PRODUCTS - SALES; MEASURED IN $')
        ORDER BY year, data_item
       "

agtotal_df <- dbGetQuery(conn, sql)

kable_styling(kable(tail(agtotal_df)),
              bootstrap_options = c("striped", "hover"))
```

```{r fig9, fig.height = 6, fig.width = 15, fig.align = "center"}
plot_mat <- with(agtotal_df, tapply(value, list(year, category), sum))

par(mar=c(5, 8, 2, 1))
barplot(plot_mat, main="U.S. Agriculture Census: Crop vs Animal Sales", cex.main=1.5,
        col=seaborn_palette[1:5], ylim=c(0, 2E11+5E10), yaxt="n", beside=TRUE)

axis(side=2, at=axTicks(2), labels=format(axTicks(2), big.mark=',', scientific=FALSE), las=1)
legend("top", row.names(plot_mat), fill=seaborn_palette[1:5], ncol=5)
```

<div style="float:right"><img src="IMAGES/world_metrics.png" width="300px"/></div>
### World Metrics

```{r}
sql <- "WITH pop AS 
            (SELECT p.year,
                    p.population
             FROM world_population p
             WHERE p.country_name = 'World'
               AND p.year BETWEEN 2000 AND 2019
             ),
              
              land AS
            (SELECT a.year,
                    a.percent_arable
             FROM arable_land a
             WHERE a.country_name = 'World'
               AND a.year BETWEEN 2000 AND 2019
             ),
         
              fauna AS
            (SELECT i.year,
                    SUM(i.species_count) AS animals_count
             FROM iucn_species_count i
             WHERE i.year BETWEEN 2000 AND 2019
             GROUP BY i.year
             ),
         
              flora AS
            (SELECT p.assessment_year as year,
                    COUNT(*) AS plants_count
             FROM plants_assessments p
             WHERE p.assessment_year BETWEEN 2000 AND 2019
               AND p.interpreted_status = 'Threatened'
             GROUP BY p.assessment_year
             ),             
         
              ice AS
            (SELECT s.date_year as year,
                    AVG(s.extent) FILTER(WHERE s.region = 'Arctic') AS arctic_sea_ice_extent,
                    AVG(s.extent) FILTER(WHERE s.region = 'Antarctica') AS antarctic_sea_ice_extent
             FROM sea_ice_extent s
             WHERE s.date_year BETWEEN 2000 AND 2019
             GROUP BY s.date_year
             ),
             
              ocean AS
            (SELECT o.year as year,
                    AVG(o.tco2) AS total_co2,
                    AVG(o.phts25p0) AS ph_scale
             FROM ocean_data o
             WHERE o.year BETWEEN 2000 AND 2019
               AND o.tco2 <> -9999 AND o.phts25p0 <> -9999
             GROUP BY o.year
             ),
             
              temp AS
            (SELECT g.year as year,
                    AVG(g.global_mean) AS global_mean
             FROM global_temperature g
             WHERE g.year BETWEEN 2000 AND 2019
             GROUP BY g.year
             )
             
         SELECT pop.year, pop.population, land.percent_arable, fauna.animals_count, flora.plants_count,
                ice.arctic_sea_ice_extent, ice.antarctic_sea_ice_extent, 
                ocean.total_co2, ocean.ph_scale, temp.global_mean
         FROM pop 
         JOIN land USING (year)
         JOIN fauna USING (year)
         JOIN flora USING (year)
         JOIN ice USING (year)
         JOIN ocean USING (year)
         JOIN temp USING (year)
         ORDER BY pop.year"

env_world_df <- dbGetQuery(conn, sql)

kable_styling(kable(tail(env_world_df)), font_size = 12,
              bootstrap_options = c("striped", "hover"))
```

```{r}
kable_styling(kable(cor(env_world_df[-1])), font_size = 12,
              bootstrap_options = c("striped", "hover"))
```

```{r fig10, fig.height = 15, fig.width = 15, fig.align = "center"}
par(mfrow=c(4,2), mai = c(0.3, 0.2, 0.7, 0.2))

for(x in colnames(env_world_df)[3:ncol(env_world_df)]) {
  lfit <- loess(paste(x, "~ population"), data=env_world_df)
  
  plot(as.formula(paste(x, "~ population")), env_world_df, 
       main=paste("population and\n", gsub("_", " ", x)), cex.main=2,
       type="p", col=seaborn_palette[1], yaxt='n', xaxt='n', pch=19)
  axis(side=1, at=axTicks(1), labels=format(axTicks(1), big.mark=',', scientific=FALSE))
  axis(side=2, at=axTicks(2), labels=format(axTicks(2), big.mark=',', scientific=FALSE))
  pop_order <- order(env_world_df$population)
  lines(env_world_df$population[pop_order], lfit$fitted[pop_order], col=seaborn_palette[4], lwd=3)
}

```


```{r}
dbDisconnect(conn)
```

<div style="float:right"><img src="IMAGES/r_shiny.png"/></div>

### User Data Application

<div style="float:center"><img src="IMAGES/env_data.png" width="400px"/></div>

```{r}

# LINUX SHELL COMMAND CALL
system(paste0("gnome-terminal -- Rscript -e \"library(shiny); setwd('", getwd(), "'); runApp('EnvironmentDB_Shiny_App.R')\""))

```

<hr style="border: none; height: 1px; background-color: #CCC;"/>

<h2><span style="color: #336791"/>Conclusion</span></h2>

<div style="float:right;"><img src="IMAGES/data_pipeline.png" width="350px"/></div>
<br/>

<div style="float:left;"><img src="IMAGES/postgresql_r.png" width="100px"/></div>
<br/>
<br/>

- <h3><span style="color: #336791"/>Postgres as tool in data science workflow</h3>
- <h3><span style="color: #336791"/>Data persistence and hygiene</h3>
- <h3><span style="color: #336791"/>Centralization and security</h3>
- <h3><span style="color: #336791"/>Efficiency and usefulness</h3>
- <h3><span style="color: #336791"/>Scalability and applicability</h3>

<br/>
<br/>


